Short-term fishery gains mask long-term resource pains: Spatial fisheries management changes promote hyperstable CPUE in Labrador snow crab <i>Chionoecetes opilio</i> during a period of heavy exploitation
Bibliographic record
Abstract
Abstract Objective The snow crab Chionoecetes opilio resource in Assessment Division 2HJ has experienced prolonged high exploitation rates and reduced exploitable biomass over the past two decades. We aimed to explore whether this poor state of the resource is associated with spatial management changes made in 2003 and 2013. Methods We tested for differences in fishery performance trends before and after the implementation of spatial management which include standardized CPUE, spatial extent of fishing effort, and size at maturity of male snow crabs. Result The results show that spatial regulatory changes were successful in increasing fishery catch rates in the short term but that chronic high exploitation eventually overrode these gains, with contracted fishing patterns leading to increased localized depletion rates on dominant stock components. This ultimately culminated in a downward shift in size at maturity and other concerning biological outcomes. Conclusion The analysis demonstrates spatial management measures contributed to the present poor state of Assessment Division 2HJ snow crab and that such measures should serve as complements to—not replacements for—stringent quota control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".